Niranjankumar-c

import sklearn.datasets
import numpy as np
import pandas as pd

breast_cancer = sklearn.datasets.load_breast_cancer()
X = breast_cancer.data
Y = breast_cancer.target

#Converting the data to Pandas dataframe
data = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)

Niranjankumar-c

class MPNeuron:
  
  def __init__(self):
    self.b = None
    
  def model(self, x):
    return(sum(x) >= self.b)
  
  def predict(self, X):
    Y = []

Niranjankumar-c

#import packages
import sklearn.datasets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

#load the breast cancer data
breast_cancer = sklearn.datasets.load_breast_cancer()

Niranjankumar-c

class Perceptron:
  
  #constructor
  def __init__ (self):
    self.w = None
    self.b = None
    
  #model  
  def model(self, x):
    return 1 if (np.dot(self.w, x) >= self.b) else 0

Niranjankumar-c

class Perceptron:
  
  #constructor
  def __init__ (self):
    self.w = None
    self.b = None
    
  #model  
  def model(self, x):
    return 1 if (np.dot(self.w, x) >= self.b) else 0

Niranjankumar-c

perceptron = Perceptron()

#epochs = 10000 and lr = 0.3
wt_matrix = perceptron.fit(X_train, Y_train, 10000, 0.3)

#making predictions on test data
Y_pred_test = perceptron.predict(X_test)

#checking the accuracy of the model
print(accuracy_score(Y_pred_test, Y_test))

Niranjankumar-c

#toy data
X = [0.5,2.5]
Y = [0.2,0.9]

import numpy as np
import matplotlib.pyplot as plt

w_values = []
b_values = []
loss_values = []

Niranjankumar-c

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error
from tqdm import tqdm_notebook

from sklearn.preprocessing import OneHotEncoder
from sklearn.datasets import make_blobs

Niranjankumar-c

#creating my own color map for better visualization
my_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["red","yellow","green"])

#Generating 1000 observations with 4 labels - multi class
data, labels = make_blobs(n_samples=1000, centers=4, n_features=2, random_state=0)
print(data.shape, labels.shape)

#visualize the data
plt.scatter(data[:,0], data[:,1], c=labels, cmap=my_cmap)
plt.show()

Niranjankumar-c

class SigmoidNeuron:
  #intialization
  def __init__(self):
    self.w = None
    self.b = None
  #forward pass  
  def perceptron(self, x):
    return np.dot(x, self.w.T) + self.b
  
  def sigmoid(self, x):